{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "precious-premises",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "peripheral-lounge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>seriesId</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
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       "      <th>2</th>\n",
       "      <td>sg4313</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg21448</td>\n",
       "      <td>5313</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg22543</td>\n",
       "      <td>46973</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>2232</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>sg28154</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>sg1777</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>sg1778</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2237 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020\n",
       "0      sg9550   76912\n",
       "1      sg3524  130906\n",
       "2      sg4313  173188\n",
       "3     sg21448    5313\n",
       "4     sg22543   46973\n",
       "...       ...     ...\n",
       "2232  sg28153     NaN\n",
       "2233  sg28154     NaN\n",
       "2234  sg20442     NaN\n",
       "2235   sg1777     NaN\n",
       "2236   sg1778     NaN\n",
       "\n",
       "[2237 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取销量数据\n",
    "df = pd.read_csv('sale_new.csv', usecols=[\"seriesId\",\"sum2020\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "stainless-advocate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除NaN数据\n",
    "df = df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "pharmaceutical-butter",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>sg22543</td>\n",
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       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>sg12794</td>\n",
       "      <td>8185</td>\n",
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       "    <tr>\n",
       "      <th>2213</th>\n",
       "      <td>sg22317</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>sg10981</td>\n",
       "      <td>6164</td>\n",
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       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>sg11763</td>\n",
       "      <td>8735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>868 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020\n",
       "0      sg9550   76912\n",
       "1      sg3524  130906\n",
       "2      sg4313  173188\n",
       "3     sg21448    5313\n",
       "4     sg22543   46973\n",
       "...       ...     ...\n",
       "2212  sg12794    8185\n",
       "2213  sg22317     117\n",
       "2214  sg10981    6164\n",
       "2224  sg11763    8735\n",
       "2226  sg25549   17164\n",
       "\n",
       "[868 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除数据值为 - 的数据\n",
    "rs = df[df['sum2020']!='-']\n",
    "rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "victorian-welsh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>22.97万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg9550</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>26.31万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>14834</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14835</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14836</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14837</th>\n",
       "      <td>sg28153</td>\n",
       "      <td>暂无报价</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14838</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>39.25万</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14839 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seriesId finalPrice\n",
       "0       sg9550     22.97万\n",
       "1       sg9550     22.97万\n",
       "2       sg9550     25.23万\n",
       "3       sg9550     25.23万\n",
       "4       sg9550     26.31万\n",
       "...        ...        ...\n",
       "14834  sg28153       暂无报价\n",
       "14835  sg28153       暂无报价\n",
       "14836  sg28153       暂无报价\n",
       "14837  sg28153       暂无报价\n",
       "14838  sg20442     39.25万\n",
       "\n",
       "[14839 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取汽车价格\n",
    "config = pd.read_csv('汽车参数_太平洋汽车_new.csv', usecols=[\"seriesId\",\"finalPrice\"])\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "sixth-indicator",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>25.23万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>26.31万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14828</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>184.61万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14829</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>184.61万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14830</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14831</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26万</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14838</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>39.25万</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14652 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seriesId finalPrice\n",
       "0       sg9550     22.97万\n",
       "1       sg9550     22.97万\n",
       "2       sg9550     25.23万\n",
       "3       sg9550     25.23万\n",
       "4       sg9550     26.31万\n",
       "...        ...        ...\n",
       "14828   sg4194    184.61万\n",
       "14829   sg4194    184.61万\n",
       "14830   sg4194    210.26万\n",
       "14831   sg4194    210.26万\n",
       "14838  sg20442     39.25万\n",
       "\n",
       "[14652 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除暂无报价的汽车数据\n",
    "config = config[config['finalPrice']!='暂无报价']\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "promotional-motor",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
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       "      <td>sg4194</td>\n",
       "      <td>184.61</td>\n",
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       "      <th>14830</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26</td>\n",
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       "      <th>14831</th>\n",
       "      <td>sg4194</td>\n",
       "      <td>210.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14838</th>\n",
       "      <td>sg20442</td>\n",
       "      <td>39.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14652 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      seriesId  finalPrice\n",
       "0       sg9550       22.97\n",
       "1       sg9550       22.97\n",
       "2       sg9550       25.23\n",
       "3       sg9550       25.23\n",
       "4       sg9550       26.31\n",
       "...        ...         ...\n",
       "14828   sg4194      184.61\n",
       "14829   sg4194      184.61\n",
       "14830   sg4194      210.26\n",
       "14831   sg4194      210.26\n",
       "14838  sg20442       39.25\n",
       "\n",
       "[14652 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去掉单位并转型为float\n",
    "config['finalPrice'] = config['finalPrice'].str.replace(\"万\",'').astype(float)\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "several-berlin",
   "metadata": {},
   "outputs": [],
   "source": [
    "lastRs = pd.merge(rs,config,on=\"seriesId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "technical-method",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>25.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>25.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sg9550</td>\n",
       "      <td>76912</td>\n",
       "      <td>26.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5791</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5792</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5793</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>sg25549</td>\n",
       "      <td>17164</td>\n",
       "      <td>12.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5796 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     seriesId sum2020  finalPrice\n",
       "0      sg9550   76912       22.97\n",
       "1      sg9550   76912       22.97\n",
       "2      sg9550   76912       25.23\n",
       "3      sg9550   76912       25.23\n",
       "4      sg9550   76912       26.31\n",
       "...       ...     ...         ...\n",
       "5791  sg25549   17164       11.81\n",
       "5792  sg25549   17164       11.81\n",
       "5793  sg25549   17164       12.69\n",
       "5794  sg25549   17164       12.93\n",
       "5795  sg25549   17164       12.93\n",
       "\n",
       "[5796 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lastRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "herbal-harassment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26398852c88>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(lastRs.finalPrice[:100],lastRs.sum2020[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "iraqi-parameter",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "annoying-generation",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
